A Skeleton-based Geometric Deep Neural Network for Alzheimer’s Disease Mice Behavioral Analysis

Alzheimer’s disease (AD) is a progressive and irreversible brain disorder that remains incurable. Research has shown a strong link between gait and cognition, with AD significantly affecting gait and behavioral patterns. While most preclinical studies analyze gait using mice pawprints, this approach is prone to inconsistencies due to variations in pawprints’ correction by different experimenters. In contrast, skeleton-based behavioral analysis provides a more consistent and cleaner representation. In this work, we collect a new mice dataset and propose a novel skeleton-based geometric deep attention network for disease classification using the mice’s behavioral information from this dataset. We begin our analysis by extracting posture data as skeleton landmark sequences which are then processed by our proposed network for the classification. Our proposed approach demonstrates promising results, making it particularly relevant for preclinical gait research and we conduct an ablation study on our proposed approach to demonstrate its effectiveness. Our work is reproducible and the codes are publicly available.<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup><sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>https://github.com/ayodejimb/Geometric_Deep_Behavioral_Analysis